1. Field of the Invention
The present invention generally relates to computerized systems used to predict and manage the network performance characteristics of wireless communication networks, and more particularly, to a method and system for determining and using the position of wireless devices and infrastructure within an environment by combining measured signal data with table lookups of corresponding positions to provide novel features that will be critical to future wireless networks.
2. Background Description
As data communications use increases, the ability to locate the position of wireless devices that are detectable by the network has quickly become an important issue for network engineers who must design, deploy, and maintain cellular telephone systems, paging systems, wireless or wired computer networks, or new wireless systems and technologies such as personal communication networks, wireless local area networks (WLANs), ultra-wideband networks, RF ID networks, ZigBee networks, and WiFi/WiMax/mesh last-mile wireless networks. Similar needs are emerging for wireless Internet Service Providers (WISPs) who need to provision and maintain wireless connections to their customers. Emergency services, network security, network troubleshooting and network quality of service are just a few of the applications of positioning technology.
The use of predictive models in designing and maintaining wireless networks is becoming a standard industry practice. Rapid engineering design, deployment, and management methods for microcell and in-building wireless systems are vital for cost-efficient build-out and on-going operation. The evolving wireless infrastructure is moving toward packet-based transmissions, and outdoor cellular may soon complement in-building Wireless LAN technology. See “Wireless Communications: Past Events and a Future Perspective” by T. S. Rappaport, et al., IEEE Communications Magazine, June 2002 (invited); and “Research Challenges in Wireless Networks: A Technical Overview, by S. Shakkottai and T. S. Rappaport at Proceeding of the Fifth International Symposium on Wireless Personal Multimedia Communications, Honolulu, Hi., October 2002 (invited).
The placement and configuration of wireless and wired equipment, such as routers, hubs, switches, cell sites, cables, antennas, distribution networks, receivers, transceivers, transmitters, repeaters, access points, or RF ID tag readers is critical from both a cost and performance standpoint. The design engineer, or a network operator, manager, or installer, whether or not technically trained, preferably should be able to predict how much interference can be expected from other wireless systems and where the interference will manifest itself within the environment, as well as locate network users or other entities. In many cases, the wireless network interferes with itself, forcing the designer to carefully analyze many different equipment configurations in order to achieve proper performance. Sometimes power cabling is only available at limited places in a building or campus, or possible equipment sites are otherwise constrained, and so decisions must be made with respect to the proper location and quantity of equipment, and their proper channel assignments and other configuration. Prediction methods which are known and which are available in the literature provide well-accepted methods for computing coverage or interference values for many cases.
Depending upon the design goals or operating preferences, the performance of a wireless communication system may involve tradeoffs or a combination of one or more factors. For example, the total area covered with adequate received or radio signal strength (RSSI), the area covered with adequate data throughput levels, and the numbers of customers that can be serviced by the system at desired qualities of service or average or instantaneous bandwidth allocations or delays are among the deciding factors used by network professionals in planning the placement of communication equipment comprising the wireless system, even though these parameters change with time and space, as well as with the number and types of users and their traffic demands.
A highly accurate method for properly determining the appropriate placement of equipment and efficient operating characteristics of a multiple-transmitter network (such as a Wireless LAN with many access points across a campus) is preferred for use in the original installation and start-up of a network. Given a reliable method for predicting the radio wave propagation environment and RF channel characteristics for any given location within the physical environment, the interaction between mobile or fixed wireless users and the communications network, the performance of any given proposed or existing communications network can be predicted. This capability enables design engineers and network architects to determine and analyze the performance of a proposed arrangement and configuration of network equipment before an investment is made to deploy the network.
The performance of a wireless communication system may be approximated by determining one or more RF channel characteristics, where RF channel characteristics refers to any measurable parameters that are typically associated with a radio channel within any communications network such as, but not limited to: path loss, the received signal strength intensity (RSSI), system noise (SNR), system interference (SIR), delay spread levels, bit error rate (BER), frame error rate (FER), packet error rate (PER), quality of service (QoS), packet throughput, packet latency, packet jitter, outage, traffic, capacity, bandwidth usage, call rate, call duration, association rate, drop call rate, block call rate, packet collision rate, handoffs, angle of arrival, power delay profile, and other well known, quantifiable characteristics. Radio frequency (RF) channel characteristics such as these are predictable using well-known techniques to those skilled in the art. Preferred methods for predicting RF channel characteristics are outlined in U.S. Pat. No. 6,317,599 entitled “Method and System for Automated Optimization of Antenna Positioning in 3-D” by Rappaport et al, and in pending U.S. patent application Ser. No. 10/830,445 entitled “System and Method for Ray Tracing Using Reception Surfaces” by Skidmore et al, both of which are hereby incorporated by reference. If there is then established a reliable transform between the RF channel characteristics and end-user transport layer performance characteristics, the end-user transport layer performance can be reliably predicted as described in pending U.S. patent application Ser. No. 10/830,446 entitled “System and Method for Predicting Network Performance and Position Using Multiple Table Lookups” by Skidmore et al.
Research efforts by many have attempted to model and predict radio wave propagation. For example, work by AT&T Laboratories, Brooklyn Polytechnic, and Virginia Tech are described in papers and technical reports entitled: S. Kim, B. J. Guarino, Jr., T. M. Willis III, V. Erceg, S. J. Fortune, R. A. Valenzuela, L. W. Thomas, J. Ling, and J. D. Moore, “Radio Propagation Measurements and Predictions Using Three-dimensional Ray Tracing in Urban Environments at 908 MHZ and 1.9 GHz,” IEEE Transactions on Vehicular Technology, vol. 48, no. 3, May 1999 (hereinafter “Radio Propagation”); L. Piazzi, H. L. Bertoni, “Achievable Accuracy of Site-Specific Path-Loss Predictions in Residential Environments,” IEEE Transactions on Vehicular Technology, vol. 48, no. 3, May 1999 (hereinafter “Site-Specific”); G. Durgin, T. S. Rappaport, H. Xu, “Measurements and Models for Radio Path Loss and Penetration Loss In and Around Homes and Trees at 5.85 GHz,” IEEE Transactions on Communications, vol. 46, no. 11, November 1998; T. S. Rappaport, M. P. Koushik, J. C. Liberti, C. Pendyala, and T. P. Subramanian, “Radio Propagation Prediction Techniques and Computer-Aided Channel Modeling for Embedded Wireless Microsystems,” ARPA Annual Report, MPRG Technical Report MPRG-TR-94-12, Virginia Tech, July 1994; T. S. Rappaport, M. P. Koushik, C. Carter, and M. Ahmed, “Radio Propagation Prediction Techniques and Computer-Aided Channel Modeling for Embedded Wireless Microsystems,” MPRG Technical Report MPRG-TR-95-08, Virginia Tech, July 1994; T. S. Rappaport, M. P. Koushik, M. Ahmed, C. Carter, B. Newhall, and N. Zhang, “Use of Topographic Maps with Building Information to Determine Antenna Placements and GPS Satellite Coverage for Radio Detection and Tracking in Urban Environments,” MPRG Technical Report MPRG-TR-95-14, Virginia Tech, September 1995; T. S. Rappaport, M. P. Koushik, M. Ahmed, C. Carter, B. Newhall, R. Skidmore, and N. Zhang, “Use of Topographic Maps with Building Information to Determine Antenna Placement for Radio Detection and Tracking in Urban Environments,” MPRG Technical Report MPRG-TR-95-19, Virginia Tech, November 1995; S. Sandhu, M. P. Koushik, and T. S. Rappaport, “Predicted Path Loss for Roslyn, Va., Second set of predictions for ORD Project on Site Specific Propagation Prediction,” MPRG Technical Report MPRG-TR-95-03, Virginia Tech, March 1995, T. S. Rappaport, et al., “Indoor Path Loss Measurements for Homes and Apartments at 2.4 and 5.85 GHz, by Wireless Valley Communications, Inc., Dec. 16, 1997; Russell Senate Office Building Study, Project Update, Roger R. Skidmore, et al., for Joseph R. Loring & Associates; “Assessment and Study of the Proposed Enhancements of the Wireless Communications Environment of the Russell Senate Office Building (RSOB) and Associated Utility Tunnels,” AoC Contract # Acbr96088, prepared for Office of the Architect of the Capitol, Feb. 20, 1997; “Getting In,” R. K. Morrow Jr. and T. S. Rappaport, Mar. 1, 2000, Wireless Review Magazine; and “Isolating Interference,” by T. S. Rappaport, May 1, 2000, Wireless Review Magazine, “Site Specific Indoor Planning” by R. K. Morrow, Jr., March 1999, Applied Microwave and Wireless Magazine, “Predicting RF coverage in large environments using ray-beam tracing and partitioning tree represented geometry,” by Rajkumar, et al, Wireless Networks, Volume 2, 1996, “Cool Cloud Wireless LAN Design Guildelines and User Traffic Modeling for In-Store Use (Part 1: System Deployment” TR November 2003, WNCG University of Texas by J. K. Chen and T. S. Rappaport, and “Cool Cloud Wireless LAN Design Guildelines and User Traffic Modeling for In-Store Use (Part 2: Traffic Statistics) by C. Na and T. S. Rappaport, November 2003. A. Verstak, N. Ramakrishnan, K. K. Bae, W. H. Tranter, L. T. Watson, J. He, C. A. Shaffer, T. S. Rappaport, “Using Hierarchical Data Mining to Characterize Performance of Wireless System Configurations”, Submitted to ACM Transactions on Modeling and Computer Simulation, August 2002.
Global Positioning System (GPS) is a well-known positioning technology that is widely used. Global Positioning System relies on a wireless device receiving radio signals from one or more orbiting satellites. By correlating the received radio signals from each satellite with pre-existing knowledge of the precise location of each satellite at the instant the signals are received, the GPS device can estimate its position on the Earth in terms of a latitude, longitude, and elevation. Existing GPS systems do not rely upon a site-specific model of the environment, nor do they contemplate the use of performance lookup table lookups to correlate measured RF channel characteristics with a position.
Airespace™ provides the Airespace Control System (ACS), a wireless local area network (WLAN) management software application that includes some limited capabilities for positioning mobile client devices. The ACS uses a crude two-dimensional model of an inbuilding environment with known positions of access points. The access points can measure received signal strength intensity (RSSI) information for wireless client devices and communicate that information to the ACS. The ACS then uses a technique referred to as fingerprinting, where the measured RSSI data for the client devices is compared with predicted RSSI data at various positions. Such a technique is limited by not considering other RF channel characteristics or the current network operating condition. In addition, the technique does not utilize a mesh with one or more performance lookup tables associated with each vertex, and does not contemplate a three-dimensional environment.
Ekahau™ provides the Real-Time Locationing System (RTLS) for determining the position of mobile wireless devices within a WLAN. The RTLS utilizes a crude two-dimensional model of an in-building environment. The RTLS relies upon large quantities of measured RSSI data that has been correlated to positions within the environment. That is, in order to determine a position using RTLS, one must first physically visit the environment in question and collect extensive measured RSSI information using an actual wireless client device. This measured RSSI information is then correlated with positions within the environment. At a later date, the RTLS compares measured RSSI data received live from wireless equipment monitoring and measuring devices with the pre-measured RSSI data. Through this comparison, the position of a wireless device is determined. The Ekahau RTLS does not contemplate using predicted RF channel characteristics or performance lookup table lookups as described herein, does not consider the current network operating condition, nor does the RTLS contemplate a three-dimensional environment.
Newbury Networks™ provides the WiFi Workplace™, a software application designed to provide enhanced security and positioning capabilities for WLANs. The WiFi Workplace utilizes a crude two-dimensional model of an in-building environment. The WiFi Workplace relies upon large quantities of measured RSSI data that has been correlated to positions within the environment. That is, in order to determine a position using WiFi Workplace, one must first physically visit the environment in question and collect extensive measured RSSI information using an actual wireless client device. This measured RSSI information is then correlated with positions within the environment. At a later date, the WiFi Workplace™ compares measured RSSI data received live from wireless equipment monitoring and measuring devices with the pre-measured RSSI data. Through this comparison, the position of a wireless device is determined. Newbury Networks™ does not contemplate using predicted RF channel characteristics or performance lookup table lookups as described herein, does not consider the current network operating condition, nor does the WiFi Workplace™ contemplate a three-dimensional environment.
In addition, various systems and methods are known in the prior art with regard to the identification of the location of mobile clients roaming on a wireless network. Such systems and methods are generally referred to as position location techniques, and are well-known in the field for their ability to use the RF characteristics of the transmit signal to or from a mobile device as a determining factor for the position of the mobile device. Various papers such as P. Bahl, V. Padmanabhan, and A. Balachandran, “A Software System for Locating Mobile Users: Design, Evaluation, and Lessons,” April 2000 presents various techniques for doing crude position location from signal strength measurements.
Other companies such as AirTight, Polaris Wireless, and the radio camera concept from US Wireless (now defunct), use signal strength to estimate the position of wireless users. U.S. Pat. No. 6,674,403 to Gray et. al., U.S. Pat. No. 6,664,925 to Moore et. al., U.S. Pat. No. 6,799,047 to Bahl et. al., U.S. Pat. No. 6,259,924 to Alexander, Jr. et. al., U.S. Pat. No. 6,256,506 to Alexander, Jr., et. al., U.S. Pat. No. 6,466,938 to Goldberg, and Patent application 20020028681 to Lee, et. al., deal with estimating position locations using databases of measurements. In addition, some of the present inventors have filed pending U.S. patent application Ser. No. 10/386,943 entitled “System and Method for Automated Configuration of Transceivers For Obtaining Desired Network Performance Objectives,” and pending U.S. patent application Ser. No. 10/830,446 entitled “System and Method for Prediction Network Performance and Position Location Using Multiple Table Lookups.”. None of the aforementioned systems, methods, or techniques contemplates the novel capabilities and features that stem from knowledge of position.